Children’s hospitals (CHs) deliver care to underserved, critically ill, and medically complex patients. However, non-CHs care for the majority of children with frequently occurring conditions. In this study, we aimed to examine resource use across hospitals where children receive care for frequent inpatient conditions.
This was a cross-sectional, observational analysis of pediatric hospitalizations for 8 frequent inpatient conditions (pneumonia, asthma, bronchiolitis, mood disorders, appendicitis, epilepsy, skin and soft tissue infections, and fluid and electrolyte disorders) in the 2016 Kids’ Inpatient Database. Primary outcomes were median length of stay (LOS) and median total cost. The primary independent variable was hospital type: nonchildren’s, nonteaching; nonchildren’s, teaching (NCT); and freestanding CHs. Multivariable linear regression was used to assess differences in mean LOS and costs.
There were 354 456 pediatric discharges for frequent inpatient conditions. NCT hospitals cared for more than one-half of all frequent inpatient conditions. CHs and NCT hospitals cared for the majority of patients with higher illness severity and medical complexity. After controlling for patient and hospital factors, discharges for frequent inpatient conditions at CHs had 0.48% longer mean LOS and 61% greater costs compared with NCT hospitals (P < .01).
CHs revealed higher estimated costs in caring for frequent inpatient conditions despite controlling for patient- and hospital-level factors but also cared for higher illness severity and medical complexity. Further research is warranted to explore whether we lack sufficient measures to control for patient-level factors and whether higher costs are justified by the specialized care at CHs.
Hospitalized children receive care in diverse settings ranging from nonacademic adult facilities to specialized children’s hospitals (CHs). Differences in these hospital settings have been described because CHs often serve as tertiary centers and provide care for underserved, critically ill, medically complex, and high-cost populations.1–5 In addition to differences in patient population, there are differences in the aggregate volume across hospital type, with only 30% of pediatric hospitalizations occurring in CHs.5,6 Although much of the current data on pediatric hospitalizations comes from CHs,7–9 CHs may not be representative of the general pediatric inpatient population.
Hospital types caring for children may also demonstrate variation in resource use. In recent literature, authors revealed commonalities in the conditions frequently resulting in hospitalizations across hospital types but found differences in the costliest conditions in CHs compared with general hospitals.6 Additionally, in a previous study, Merenstein et al10 found that freestanding CHs had higher charges for common conditions, despite adjusting for population characteristics. However, cost was not examined. Building on these previous studies, further research is needed to examine whether accounting for patient and hospital factors can ameliorate cost differences in frequent inpatient conditions. Understanding resource-use patterns across all hospital types that care for children has the potential to inform pediatric research priorities and reimbursement policy decisions. Our study aims were to describe the patient and hospital characteristics for frequent inpatient conditions across hospital types that care for children and to examine the associated resource use.
Methods
Study Population, Design, and Data Source
This was a cross-sectional, observational analysis of pediatric hospitalizations for frequent inpatient conditions in the United States using the 2016 Healthcare Cost and Utilization Project (HCUP) Kids’ Inpatient Database (KID), which is the most current available.11 We included all discharges for patients <18 years of age with a primary diagnosis of pneumonia, asthma, acute bronchiolitis, asthma, mood disorders, appendicitis, epilepsy, skin and soft tissue infections (SSTI), or fluid and electrolyte disorders, identified by HCUP Clinical Classification Software Refined, and we excluded hospitalizations for neonates identified by All Patient Refined Diagnostic Related Groups.4 Clinical Classifications Software Refined is based on the International Classification of Diseases, 10th Revision, Clinical Modification and collapses related diagnosis codes into the principal category. The included frequent inpatient conditions were chosen on the basis of the acute care conditions most frequently accounting for pediatric hospitalizations within KID.12
KID is maintained by the Agency for Healthcare Research and Quality.11 It includes data from CHs and non-CHs, patient demographics, hospital characteristics, diagnoses, lengths of stay (LOSs), and charges.11 The institutional review board at Baylor College of Medicine determined this deidentified data did not constitute human subjects research.
Primary Outcomes
Primary outcomes were LOS and total cost of hospitalization. LOS was determined by subtracting the admission from the discharge date and was measured in days, and the minimum LOS was 1 day, even if admission and discharge occurred on the same calendar day. Hospital charge was the total amount charged by the hospital. The HCUP cost-to-charge ratio file provided hospital-specific ratios, allowing for the conversion of charges to estimated costs.
Independent Variables
The primary independent variable was hospital type, categorized as nonchildren’s, nonteaching (NCNT); nonchildren’s, teaching (NCT); and freestanding CHs, as classified by the American Hospital Association.11 Patient and hospital characteristics consisted of age, race or ethnicity, sex, insurance type, Hospitalization Resource Intensity Scores for Kids (H-RISK)13 severity of illness, median zip code–associated income, hospital bed size, and number of complex chronic conditions (CCCs).14
Analysis
Analyses were performed by using SAS 9.4 (SAS Institute, Inc, Cary, NC). Because of skewed distribution of data toward higher LOS and cost before and after log transformation, median values were examined in unadjusted analysis. A Rao-Scott χ2 test was used to compare categorical variables, and a Kruskal-Wallis test was used to compare medians of continuous variables across hospital types. Survey-weighted multivariable linear regression with log transformation was used to assess differences in mean LOS and costs, controlling for patient and hospital characteristics. LOS was controlled for in-cost analysis. We considered each hospital as a cluster for the purpose of calculating SEs. Results were reported as estimates with P values (P < .05 was considered significant).
Results
Hospitalization Characteristics
In 2016, there were 354 456 pediatric discharges for frequent inpatient conditions (Table 1). NCT hospitals were the most frequent discharging hospital type (56%). Non-CHs combined (NCNT and NCT) cared for >70% of frequent inpatient condition discharges. Payer-mix distribution was similar, with nearly 60% of discharges attributable to public payers within each hospital type. NCT hospitals and CHs cared for a higher H-RISK severity and a greater proportion of children with CCCs. Approximately one-third of discharges for frequent inpatient conditions in CHs had ≥2 CCCs. Pneumonia accounted for the highest proportion of discharges at NCNT hospitals compared with acute bronchiolitis within NCT hospitals and CHs (Table 2).
. | n (% of Total) . | NCNT Hospital, n (% Within Hospital Type) . | NCT Hospital, n (% Within Hospital Type) . | CH, n (% Within Hospital Type) . | Pa . |
---|---|---|---|---|---|
Hospitals | 3203 | 2169 | 971 | 63 | — |
Observations | 354 456 | 64 448 (18.18) | 198 088 (55.89) | 91 920 (25.93) | — |
Age, y | <.0001 | ||||
0–<1 | 80 822 (22.80) | 15 916 (24.70) | 43 403 (21.91) | 21 503 (23.39) | — |
1–4 | 118 560 (33.45) | 21 079 (32.70) | 66 381 (33.51) | 31 100 (33.83) | — |
5–9 | 70 824 (19.98) | 11 969 (18.57) | 39 409 (19.89) | 19 446 (21.16) | — |
10–14 | 52 606 (14.84) | 9126 (14.16) | 30 092 (15.19) | 13 388 (14.56) | — |
15–17 | 31 644 (8.93) | 6358 (9.87) | 18 803 (9.49) | 6483 (7.05) | — |
Race or ethnicity | <.0001 | ||||
White | 147 536 (41.62) | 31 800 (49.34) | 82 164 (41.48) | 33 572 (36.52) | — |
Black | 65 030 (18.35) | 8703 (13.50) | 40 797 (20.60) | 15 530 (16.90) | — |
Hispanic | 81 057 (22.87) | 14 978 (23.24) | 44 015 (22.22) | 22 064 (24.00) | — |
Other | 33 966 (9.58) | 5530 (8.58) | 21 688 (10.95) | 6748 (7.34) | — |
Missing | 26 867 (7.58) | 3437 (5.33) | 9424 (4.76) | 14 006 (15.24) | — |
Sex | .0248 | ||||
Female | 155 008 (43.73) | 27 921 (43.32) | 86 577 (43.71) | 40 510 (44.07) | — |
Male | 199 415 (56.26) | 36 519 (56.66) | 111 497 (56.29) | 51 399 (55.92) | — |
Missing | 33 (0.01) | 8 (0.01) | 14 (0.01) | 11 (0.01) | — |
Insurance type | <.0001 | ||||
Public | 2 103 448 (59.34) | 38 923 (60.39) | 117 635 (59.39) | 53 786 (58.51) | — |
Private | 124 352 (35.08) | 21 737 (33.73) | 69 582 (35.13) | 33 033 (35.94) | — |
Self-pay | 8231 (2.32) | 1673 (2.60) | 4198 (2.12) | 2360 (2.57) | — |
Other | 11 105 (3.13) | 1969 (3.06) | 6520 (3.29) | 2616 (2.85) | — |
Missing | 424 (0.12) | 146 (0.23) | 153 (0.08) | 125 (0.14) | — |
H-RISK median | 0.7099 | 0.5724 | 0.7099 | 0.7188 | <.0001 |
Median zip code–associated income | <.0001 | ||||
Quartile 1 (lowest) | 122 553 (34.57) | 26 023 (40.38) | 67 006 (33.83) | 29 524 (32.12) | — |
Quartile 2 | 84 546 (23.85) | 17 336 (26.90) | 46 224 (23.34) | 20 986 (22.83) | — |
Quartile 3 | 78 967 (22.28) | 12 554 (19.48) | 44 968 (22.70) | 21 445 (23.33) | — |
Quartile 4 (highest) | 64 287 (18.14) | 7419 (11.51) | 37 802 (19.08) | 19 066 (20.74) | — |
Unknown | 4103 (1.16) | 1116 (1.73) | 2088 (1.05) | 899 (0.98) | — |
Hospital bed size | <.0001 | ||||
Small | 42 034 (11.86) | 5925 (9.19) | 13 109 (6.62) | 23 000 (25.02) | — |
Medium | 82 021 (23.14) | 15 141 (23.49) | 36 720 (18.54) | 30 160 (32.81) | — |
Large | 230 401 (65.00) | 43 382 (67.31) | 148 259 (74.85) | 38 760 (42.17) | — |
No. CCCs | <.0001 | ||||
0 | 147 608 (41.64) | 34 908 (54.16) | 78 286 (39.52) | 34 414 (37.44) | — |
1 | 111 217 (31.38) | 20 090 (31.17) | 63 934 (32.28) | 27 193 (29.58) | — |
2+ | 95 631 (26.98) | 9450 (14.66) | 55 868 (28.20) | 30 313 (32.98) | — |
Transfers in | <.0001 | ||||
0 | 307 708 (86.81) | 61 133 (94.86) | 166 864 (84.24) | 79 711 (86.72) | — |
1 | 38 949 (10.99) | 2401 (3.73) | 25 792 (13.02) | 10 756 (11.70) | — |
2 | 6509 (1.84) | 643 (1.00) | 4491 (2.27) | 1375 (1.50) | — |
Missing | 1290 (0.36) | 271 (0.42) | 941 (0.48) | 78 (0.08) | — |
Transfers out | <.0001 | ||||
0 | 347 462 (98.03) | 61 913 (96.07) | 194 304 (98.09) | 91 245 (99.27) | — |
1 | 4976 (1.40) | 2196 (3.41) | 2495 (1.26) | 285 (0.31) | — |
2 | 1953 (0.55) | 308 (0.48) | 1261 (0.64) | 384 (0.42) | — |
Missing | 65 (0.02) | 31 (0.05) | 28 (0.01) | 6 (0.01) | — |
. | n (% of Total) . | NCNT Hospital, n (% Within Hospital Type) . | NCT Hospital, n (% Within Hospital Type) . | CH, n (% Within Hospital Type) . | Pa . |
---|---|---|---|---|---|
Hospitals | 3203 | 2169 | 971 | 63 | — |
Observations | 354 456 | 64 448 (18.18) | 198 088 (55.89) | 91 920 (25.93) | — |
Age, y | <.0001 | ||||
0–<1 | 80 822 (22.80) | 15 916 (24.70) | 43 403 (21.91) | 21 503 (23.39) | — |
1–4 | 118 560 (33.45) | 21 079 (32.70) | 66 381 (33.51) | 31 100 (33.83) | — |
5–9 | 70 824 (19.98) | 11 969 (18.57) | 39 409 (19.89) | 19 446 (21.16) | — |
10–14 | 52 606 (14.84) | 9126 (14.16) | 30 092 (15.19) | 13 388 (14.56) | — |
15–17 | 31 644 (8.93) | 6358 (9.87) | 18 803 (9.49) | 6483 (7.05) | — |
Race or ethnicity | <.0001 | ||||
White | 147 536 (41.62) | 31 800 (49.34) | 82 164 (41.48) | 33 572 (36.52) | — |
Black | 65 030 (18.35) | 8703 (13.50) | 40 797 (20.60) | 15 530 (16.90) | — |
Hispanic | 81 057 (22.87) | 14 978 (23.24) | 44 015 (22.22) | 22 064 (24.00) | — |
Other | 33 966 (9.58) | 5530 (8.58) | 21 688 (10.95) | 6748 (7.34) | — |
Missing | 26 867 (7.58) | 3437 (5.33) | 9424 (4.76) | 14 006 (15.24) | — |
Sex | .0248 | ||||
Female | 155 008 (43.73) | 27 921 (43.32) | 86 577 (43.71) | 40 510 (44.07) | — |
Male | 199 415 (56.26) | 36 519 (56.66) | 111 497 (56.29) | 51 399 (55.92) | — |
Missing | 33 (0.01) | 8 (0.01) | 14 (0.01) | 11 (0.01) | — |
Insurance type | <.0001 | ||||
Public | 2 103 448 (59.34) | 38 923 (60.39) | 117 635 (59.39) | 53 786 (58.51) | — |
Private | 124 352 (35.08) | 21 737 (33.73) | 69 582 (35.13) | 33 033 (35.94) | — |
Self-pay | 8231 (2.32) | 1673 (2.60) | 4198 (2.12) | 2360 (2.57) | — |
Other | 11 105 (3.13) | 1969 (3.06) | 6520 (3.29) | 2616 (2.85) | — |
Missing | 424 (0.12) | 146 (0.23) | 153 (0.08) | 125 (0.14) | — |
H-RISK median | 0.7099 | 0.5724 | 0.7099 | 0.7188 | <.0001 |
Median zip code–associated income | <.0001 | ||||
Quartile 1 (lowest) | 122 553 (34.57) | 26 023 (40.38) | 67 006 (33.83) | 29 524 (32.12) | — |
Quartile 2 | 84 546 (23.85) | 17 336 (26.90) | 46 224 (23.34) | 20 986 (22.83) | — |
Quartile 3 | 78 967 (22.28) | 12 554 (19.48) | 44 968 (22.70) | 21 445 (23.33) | — |
Quartile 4 (highest) | 64 287 (18.14) | 7419 (11.51) | 37 802 (19.08) | 19 066 (20.74) | — |
Unknown | 4103 (1.16) | 1116 (1.73) | 2088 (1.05) | 899 (0.98) | — |
Hospital bed size | <.0001 | ||||
Small | 42 034 (11.86) | 5925 (9.19) | 13 109 (6.62) | 23 000 (25.02) | — |
Medium | 82 021 (23.14) | 15 141 (23.49) | 36 720 (18.54) | 30 160 (32.81) | — |
Large | 230 401 (65.00) | 43 382 (67.31) | 148 259 (74.85) | 38 760 (42.17) | — |
No. CCCs | <.0001 | ||||
0 | 147 608 (41.64) | 34 908 (54.16) | 78 286 (39.52) | 34 414 (37.44) | — |
1 | 111 217 (31.38) | 20 090 (31.17) | 63 934 (32.28) | 27 193 (29.58) | — |
2+ | 95 631 (26.98) | 9450 (14.66) | 55 868 (28.20) | 30 313 (32.98) | — |
Transfers in | <.0001 | ||||
0 | 307 708 (86.81) | 61 133 (94.86) | 166 864 (84.24) | 79 711 (86.72) | — |
1 | 38 949 (10.99) | 2401 (3.73) | 25 792 (13.02) | 10 756 (11.70) | — |
2 | 6509 (1.84) | 643 (1.00) | 4491 (2.27) | 1375 (1.50) | — |
Missing | 1290 (0.36) | 271 (0.42) | 941 (0.48) | 78 (0.08) | — |
Transfers out | <.0001 | ||||
0 | 347 462 (98.03) | 61 913 (96.07) | 194 304 (98.09) | 91 245 (99.27) | — |
1 | 4976 (1.40) | 2196 (3.41) | 2495 (1.26) | 285 (0.31) | — |
2 | 1953 (0.55) | 308 (0.48) | 1261 (0.64) | 384 (0.42) | — |
Missing | 65 (0.02) | 31 (0.05) | 28 (0.01) | 6 (0.01) | — |
—, not applicable.
P refers to comparison across hospital types.
. | n (% of Total) . | NCNT Hospitals, n (% Within Hospital Type) . | NCT Hospitals, n (% Within Hospital Type) . | CHs, n (% Within Hospital Type) . | Pa . |
---|---|---|---|---|---|
Total (% across hospital types) | 354 456 | 64 448 (18.18) | 198 088 (55.89) | 91 920 (25.93) | <.0001 |
Pneumonia | 61 576 (17.37) | 16 180 (25.11) | 31 682 (15.99) | 13 714 (14.92) | <.0001 |
Acute bronchiolitis | 74 567 (21.04) | 14 381 (22.31) | 40 108 (20.25) | 20 078 (21.84) | <.0001 |
Asthma | 62 509 (17.64) | 9604 (14.90) | 36 844 (18.60) | 16 061 (17.47) | <.0001 |
Mood disorders | 16 690 (4.71) | 3066 (4.76) | 11 887 (6.00) | 1737 (1.89) | <.0001 |
Appendicitis | 35 445 (10.00) | 8262 (12.82) | 18 387 (9.82) | 8976 (9.57) | <.0001 |
Epilepsy | 47 711 (13.46) | 2280 (3.54) | 28 875 (14.58) | 16 556 (18.01) | <.0001 |
SSTI | 33 441 (9.43) | 6161 (9.56) | 18 107 (9.14) | 9173 (9.98) | <.0001 |
Fluid and electrolyte disorders | 22 517 (6.35) | 4514 (7.00) | 12 198 (6.16) | 5805 (6.32) | <.0001 |
. | n (% of Total) . | NCNT Hospitals, n (% Within Hospital Type) . | NCT Hospitals, n (% Within Hospital Type) . | CHs, n (% Within Hospital Type) . | Pa . |
---|---|---|---|---|---|
Total (% across hospital types) | 354 456 | 64 448 (18.18) | 198 088 (55.89) | 91 920 (25.93) | <.0001 |
Pneumonia | 61 576 (17.37) | 16 180 (25.11) | 31 682 (15.99) | 13 714 (14.92) | <.0001 |
Acute bronchiolitis | 74 567 (21.04) | 14 381 (22.31) | 40 108 (20.25) | 20 078 (21.84) | <.0001 |
Asthma | 62 509 (17.64) | 9604 (14.90) | 36 844 (18.60) | 16 061 (17.47) | <.0001 |
Mood disorders | 16 690 (4.71) | 3066 (4.76) | 11 887 (6.00) | 1737 (1.89) | <.0001 |
Appendicitis | 35 445 (10.00) | 8262 (12.82) | 18 387 (9.82) | 8976 (9.57) | <.0001 |
Epilepsy | 47 711 (13.46) | 2280 (3.54) | 28 875 (14.58) | 16 556 (18.01) | <.0001 |
SSTI | 33 441 (9.43) | 6161 (9.56) | 18 107 (9.14) | 9173 (9.98) | <.0001 |
Fluid and electrolyte disorders | 22 517 (6.35) | 4514 (7.00) | 12 198 (6.16) | 5805 (6.32) | <.0001 |
P refers to comparison across hospital types.
Resource Use
In unadjusted analysis, the median LOS was 2.00 days at all hospital types with interquartile range (IQR): 1.00–4.00 at CH compared with IQR: 1.00–3.00 at NCT and NCNT hospitals. Median total costs at CHs were $6298 (IQR: $3643–$11 468) compared with $3725 (IQR: $2252–$6583) at NCT hospitals. After controlling for patient and hospital factors, discharges for frequent inpatient conditions at CHs had 0.48% longer mean LOSs and 61% greater costs compared with NCTs (P < .01). LOS and costs for frequent inpatient conditions at NCNT were not significantly different compared with NCT. Similar findings were seen for each of the individual conditions, with CHs having 28% to 108% significantly higher costs than NCT hospitals (Table 3).
. | NCNT Hospitals . | P . | NCT Hospitals . | CHs . | P . |
---|---|---|---|---|---|
Percentage change in mean LOSa | |||||
Frequent inpatient conditions combined | −0.05 | .06 | Reference | 0.48 | <.01 |
Pneumonia | −0.03 | .18 | Reference | 0.51 | <.01 |
Acute bronchiolitis | −0.05 | .09 | Reference | 0.51 | <.01 |
Asthma | −0.12 | <.01 | Reference | 0.37 | <.01 |
Mood disorders | −0.07 | .39 | Reference | 0.73 | <.01 |
Appendicitis | 0.09 | <.01 | Reference | 0.25 | <.01 |
Epilepsy | −0.21 | <.01 | Reference | 0.50 | <.01 |
SSTI | −0.06 | .03 | Reference | 0.43 | <.01 |
Fluid and electrolyte disorders | −0.11 | <.01 | Reference | 0.55 | <.01 |
Percentage change in mean estimated total costsb | |||||
Frequent inpatient conditions combined | −4.44 | .06 | Reference | 60.87 | <.01 |
Pneumonia | −3.00 | .18 | Reference | 66.20 | <.01 |
Acute bronchiolitis | −4.80 | .09 | Reference | 65.88 | <.01 |
Asthma | −11.10 | <.01 | Reference | 44.40 | <.01 |
Mood disorders | −6.80 | .39 | Reference | 107.53 | <.01 |
Appendicitis | 9.30 | <.01 | Reference | 28.25 | <.01 |
Epilepsy | −18.90 | <.01 | Reference | 64.82 | <.01 |
SSTI | −5.50 | .03 | Reference | 53.20 | <.01 |
Fluid and electrolyte disorders | −10.40 | <.01 | Reference | 72.53 | <.01 |
. | NCNT Hospitals . | P . | NCT Hospitals . | CHs . | P . |
---|---|---|---|---|---|
Percentage change in mean LOSa | |||||
Frequent inpatient conditions combined | −0.05 | .06 | Reference | 0.48 | <.01 |
Pneumonia | −0.03 | .18 | Reference | 0.51 | <.01 |
Acute bronchiolitis | −0.05 | .09 | Reference | 0.51 | <.01 |
Asthma | −0.12 | <.01 | Reference | 0.37 | <.01 |
Mood disorders | −0.07 | .39 | Reference | 0.73 | <.01 |
Appendicitis | 0.09 | <.01 | Reference | 0.25 | <.01 |
Epilepsy | −0.21 | <.01 | Reference | 0.50 | <.01 |
SSTI | −0.06 | .03 | Reference | 0.43 | <.01 |
Fluid and electrolyte disorders | −0.11 | <.01 | Reference | 0.55 | <.01 |
Percentage change in mean estimated total costsb | |||||
Frequent inpatient conditions combined | −4.44 | .06 | Reference | 60.87 | <.01 |
Pneumonia | −3.00 | .18 | Reference | 66.20 | <.01 |
Acute bronchiolitis | −4.80 | .09 | Reference | 65.88 | <.01 |
Asthma | −11.10 | <.01 | Reference | 44.40 | <.01 |
Mood disorders | −6.80 | .39 | Reference | 107.53 | <.01 |
Appendicitis | 9.30 | <.01 | Reference | 28.25 | <.01 |
Epilepsy | −18.90 | <.01 | Reference | 64.82 | <.01 |
SSTI | −5.50 | .03 | Reference | 53.20 | <.01 |
Fluid and electrolyte disorders | −10.40 | <.01 | Reference | 72.53 | <.01 |
P refers to comparison between hospital types.
Adjusted for age, race, insurance type, sex, hospital bed size, number of complex conditions, income, and H-RISK in LOS analysis.
Adjusted for age, race, insurance type, sex, hospital bed size, number of complex conditions, income, H-RISK, and LOS for in-cost analysis.
Discussion
In this pediatric study for frequent inpatient conditions, we found a 61% higher cost for frequent inpatient conditions in CH compared with NCT hospitals in adjusted analysis. Our findings are consistent with previous findings10 and further the understanding of resource use using costs rather than charges (because costs more closely approximate the monetary value of the care rather than what hospitals billed for services). There are multiple potential explanations for the differences in costs between CHs and NCT hospitals. First, it is possible that CHs provide higher cost, specialized care that is not commonly available at NCT hospitals. In our study, we found the highest H-RISK score and a significant proportion of discharges with ≥2 CCCs in CHs. Current literature reveals CHs continue to serve as a safety net for specialized and vulnerable populations. In recent work by Lopez et al4 the authors demonstrated that CHs cared for a disproportionate volume, cost, and diversity of conditions compared with general hospitals. CHs also cared for the majority of nonneonatal high-cost hospitalizations across all hospitals, including those requiring ventilator support or tracheostomy with ventilatory support for >96 hours with extensive procedure or extracorporeal membrane oxygenation.4 Thus, it is possible that we still lack sufficient measures to control for patient-level factors and that higher costs are warranted for the specialized care at CHs.
An alternative is that CHs are more costly in delivering standard care for frequent inpatient conditions compared with NCT hospitals. CHs may have a higher intensity of care and services, potentially related to academic mission, including more subspecialty consultations, diagnostic testing, and broad spectrum antimicrobial therapy.15,16 In complicated pneumonia, hospital-specific differences in clinical management have been revealed to be a key factor in hospitalization cost variations.17 Without data on patient outcomes, it is difficult to interpret whether high intensity of services and higher cost of care at CH is necessary or whether it is a result of overuse (unnecessary care resulting in higher resource use).18,19 Within pediatric hospitalizations at CH, recent data demonstrate significant variability in the use of non–evidence-based testing and treatment of frequent inpatient conditions such as asthma and bronchiolitis.20 Parikh et al20 established use targets across CHs to decrease the use of nonrecommended management for hospitalized children with frequent inpatient conditions. On the basis of criteria established in evidence-based guidelines, diagnostic testing and treatment rates at top-performing CHs were used to establish achievable benchmarks for their peers.20 Expansion of this work to NCNT and NCT hospitals could help inform practice and establish new targets, standardize inpatient care, and support judicious resource use for frequent inpatient conditions across all hospital types.
There were methodologic limitations to our study. The data within KID do not include observation status stays, and we do not know how this may impact resource-use comparisons. It was also not possible to compare outcomes such as readmissions across hospital types because of limitations of KID. Additionally, cost-to-charge ratios were used, but these calculations may not fully estimate the true cost, particularly in non-CHs, where cost-shifting may occur to adult hospitalizations, and results must be interpreted with the understanding that costs accrued by the hospital may not reflect what hospitals are actually paid for services. Our study has the limitations inherent to using administrative data sources, including coding errors, missing variables, and possible misclassifications of hospital types. Additionally, because of database limitations, in our study, we specifically compare freestanding CHs with other hospital types, and nonfreestanding CHs were included in the NCT category.
Conclusions
CHs revealed higher estimated costs in caring for frequent inpatient conditions despite controlling for patient- and hospital-level factors. Further research is warranted to explore whether we lack sufficient measures to control for patient-level factors and whether higher costs are justified by the specialized care at CHs.
Future efforts should be made to continue to explore differences in care across all hospitals that treat children.
Drs Lopez and Raphael participated in study conception and design, data interpretation, and manuscript drafting and revision; Drs Yu and Kowalkowski and Ms Walder participated in study design, data entry, analysis, and interpretation, and manuscript revision; Dr Colvin participated in data interpretation and manuscript drafting and revision; and all authors approved the final manuscript as submitted.
FUNDING: No external funding.
References
Competing Interests
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
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